Electroencephalography (EEG) is widely utilized in Brain-Computer Interface (BCI) applications for detecting and interpreting brain activity to control external devices. Despite its advantages, EEG signals are often contaminated with artifacts such as Electrooculogram (EOG), Electromyogram (EMG), and Electrocardiogram (ECG) signals, which complicate data analysis and reduce classification accuracy. This review focuses on adaptive learning algorithms and investigates multiple ways to reduce these artifacts. The effectiveness of several techniques is assessed, exposing differences in precision amongst strategies. According to our research, adaptive learning algorithms have potential for reducing artifacts, but how well they work will largely rely on the particular method and application setting. The study emphasizes how crucial it is to choose the right artifact removal methods in order to improve the accuracy and dependability of BCI systems that are based on EEG.